In [1]:
#关闭警告信息
import warnings  
warnings.filterwarnings("ignore")
In [2]:
#测试GPU可用性
import tensorflow as tf
print('GPU可用性:',tf.test.is_gpu_available())

WARNING:tensorflow:From /tmp/ipykernel_822/2381302516.py:3: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU可用性: True

2025-05-31 18:12:45.081025: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 AVX512F FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2025-05-31 18:12:46.052509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /device:GPU:0 with 30987 MB memory:  -> device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:67:02.0, compute capability: 7.0

读取数据

In [2]:
#加载预处理后的数据
import pandas as pd
trainmap = pd.read_csv('/mnt/workspace/DNA_methylation_Data/trainmap.csv')
traindata = pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_Data.pkl')
dna_methylation_cg=pd.read_excel('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_CG.xlsx')
In [3]:
#拼接CG编号
CG_ID_str=['sample_id']+list(dna_methylation_cg.loc[:,'cpgsite'])
traindata.columns=CG_ID_str
traindata.head(10)
Out[3]:
sample_id cg00050873 cg00212031 cg00213748 cg00214611 cg00455876 cg01707559 cg02004872 cg02011394 cg02050847 ... cg12735498 cg12737588 cg12738347 cg12758090 cg12766407 cg12789522 cg12793879 cg12794168 cg12799119 cg12848808
0 train10001 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.096055 4.051632 -0.355631 1.157883 -9.210440 -2.921730 -3.472874 1.202370 -5.773449 -0.164336
1 train10002 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.451424 4.178048 -0.144221 0.944243 -3.938986 -2.732410 -3.406479 0.866223 -4.489850 -0.285874
2 train10003 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.241113 4.402578 -0.562248 0.924494 -5.773449 -2.767818 -3.619579 1.098346 -4.585271 -0.326813
3 train10004 NaN NaN NaN NaN NaN -1.398461 NaN NaN NaN ... -0.184485 3.744756 -1.087711 1.515914 -5.273603 -3.580953 -3.284902 1.179996 -4.489850 -0.570906
4 train10005 1.136022 -4.489850 NaN -3.993781 1.312567 -2.901295 -4.585271 2.804368 2.985388 ... -0.160310 3.744756 -0.866223 1.529517 -5.273603 -2.823096 -3.406479 1.294708 -4.690541 -0.132166
5 train10006 1.423834 -9.210440 1.035353 -3.837361 1.288795 -3.100385 -5.093549 2.767818 1.848299 ... 0.003999 4.178048 -1.536363 1.130596 -9.210440 -2.804368 -3.619579 1.157883 -4.807960 -0.885489
6 train10007 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.092047 4.247583 -0.200630 1.613763 -5.093549 -2.861514 -3.543689 1.109037 -5.773449 -0.164336
7 train10008 1.475799 -3.993781 NaN -4.051632 1.475799 -3.314031 -4.807960 2.785946 2.196336 ... -0.241113 4.402578 -0.532105 1.550147 -5.093549 -3.100385 -3.374769 1.125184 -4.489850 0.220849
8 train10009 NaN NaN NaN NaN NaN NaN NaN NaN NaN ... -0.212757 3.938986 -1.342748 1.423834 -4.585271 -3.007447 -3.472874 1.208006 -4.247583 -0.502197
9 train10010 1.515914 -4.051632 NaN -4.585271 1.174443 -2.681278 -4.112908 3.406479 2.069693 ... -0.372163 4.807960 -0.570906 1.423834 -6.811545 -2.804368 -3.472874 1.136022 -4.247583 -0.703513

10 rows × 100001 columns

数据预处理：处理空缺数据、数据拼接、转化数据类型

In [4]:
#统计数据空缺值
traindata_null=traindata.isnull().sum().sum()    #统计前10万个特征缺失值
data_sum=traindata.shape[0]*traindata.shape[1]
traindata_null_rate=traindata_null/data_sum    #计算特征缺失率
print('训练数据缺失数据量为:{0},数据总量为:{1}'.format(traindata_null,data_sum))
print('训练数据缺失率为:{0}%'.format(round(traindata_null_rate*100,4)))

训练数据缺失数据量为:46058632,数据总量为:823308233
训练数据缺失率为:5.5943%
In [5]:
#缺失数据处理
traindata.fillna(0,inplace=True)    #训练集预处理
In [6]:
# 拼接数据集
traindata=traindata.merge(trainmap[['sample_id', 'age', 'gender', 'sample_type', 'disease']],on='sample_id',how='left')
In [8]:
pd.isnull(traindata).sum()
Out[8]:
sample_id       0
cg00050873      0
cg00212031      0
cg00213748      0
cg00214611      0
               ..
cg12848808      0
age             0
gender         69
sample_type     0
disease         0
Length: 100005, dtype: int64
In [10]:
traindata['gender'].value_counts()
Out[10]:
gender
F    4409
M    3755
Name: count, dtype: int64
In [11]:
traindata.shape
Out[11]:
(8233, 100005)
In [9]:
#统计数据中患病数据和患病数据汇总
sample_type_sum=traindata['sample_type'].value_counts()    #统计患病数据总量
print('-----------------患病数据总量----------------')
print(sample_type_sum)
disease_sum=traindata['disease'].value_counts()    #汇总患病数据
print('-----------------患病数据汇总----------------')
print(disease_sum)

-----------------患病数据总量----------------
sample_type
control           6266
disease tissue    1967
Name: count, dtype: int64
-----------------患病数据汇总----------------
disease
control                 6266
Alzheimer's disease      737
schizophrenia            381
Parkinson's disease      266
rheumatoid arthritis     159
stroke                   147
Huntington's disease     135
Graves' disease           58
type 2 diabetes           46
Sjogren's syndrome        38
Name: count, dtype: int64
In [9]:
#读取卒中DNA甲基化数据
Stroke_Data=traindata.loc[traindata.loc[:,'disease']=='stroke',:]
Stroke_Data.shape
Out[9]:
(147, 100005)
In [10]:
#读取卒中DNA甲基化数据
Normal_Data=traindata.loc[traindata.loc[:,'disease']=='control',:]
Normal_Data.shape
Out[10]:
(6266, 100005)
In [11]:
#整合数据
Analysis_Data=pd.concat([Stroke_Data,Normal_Data],axis=0)
Analysis_Data.shape
Out[11]:
(6413, 100005)
In [12]:
#数据类型转化
disease_mapping = {
    'control': 0,
    'stroke': 1,
}    #构建数据对应关系
sample_type_mapping = {'control': 0, 'disease tissue': 1}
gender_mapping = {'F': 0, 'M': 1}
#训练集转化
Analysis_Data['disease_encode']=Analysis_Data['disease'].map(disease_mapping)
Analysis_Data['sample_type_encode']=Analysis_Data['sample_type'].map(sample_type_mapping)
Analysis_Data['gender_encode']=Analysis_Data['gender'].map(gender_mapping)
In [18]:
#输出仅包含卒中和正常组的数据
Analysis_Data.to_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Analysis_Data.to_csv('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.csv')

读取数据

In [1]:
import pandas as pd
Data=pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Data.head(10)
Out[1]:
sample_id cg00050873 cg00212031 cg00213748 cg00214611 cg00455876 cg01707559 cg02004872 cg02011394 cg02050847 ... cg12794168 cg12799119 cg12848808 age gender sample_type disease disease_encode sample_type_encode gender_encode
8040 train18041 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 ... 0.804620 -3.406479 -1.991711 78.0 F disease tissue stroke 1 1 0.0
8041 train18042 2.049755 -3.472874 0.000000 -3.837361 0.974195 -2.375206 -3.659672 3.790034 4.690541 ... 0.871025 -3.993781 -1.642924 52.0 M disease tissue stroke 1 1 1.0
8042 train18043 2.196336 -2.985388 0.000000 -2.963760 0.984269 -2.324893 -3.790034 3.790034 4.247583 ... 0.994389 -4.051632 -2.059685 92.0 M disease tissue stroke 1 1 1.0
8043 train18044 0.000000 0.000000 0.000000 0.000000 0.000000 -1.242189 0.000000 0.000000 0.000000 ... 1.392179 -3.886935 -2.648415 87.0 F disease tissue stroke 1 1 0.0
8044 train18045 2.632328 -3.284902 2.120936 -3.938986 1.628272 -2.616463 -3.886935 2.942546 4.247583 ... 1.621000 -3.701353 -2.681278 87.0 M disease tissue stroke 1 1 1.0
8045 train18046 2.264792 -2.942546 0.000000 -3.052934 0.979226 -1.972920 -3.659672 3.543689 4.489850 ... 1.191150 -3.619579 -1.991711 41.0 M disease tissue stroke 1 1 1.0
8046 train18047 0.000000 0.000000 0.000000 0.000000 0.000000 -1.098346 0.000000 0.000000 0.000000 ... 1.436670 -3.744756 -2.120936 78.0 F disease tissue stroke 1 1 0.0
8047 train18048 1.963624 -4.178048 0.000000 -4.178048 0.124134 -2.324893 -5.492861 3.076403 4.940737 ... 1.635580 -3.837361 -1.954393 80.0 M disease tissue stroke 1 1 1.0
8048 train18049 1.874054 0.000000 0.000000 0.000000 -0.298130 -1.798190 -4.112908 3.993781 4.585271 ... 1.703036 -3.406479 -2.131436 69.0 M disease tissue stroke 1 1 1.0
8049 train18050 1.462650 -3.256540 0.000000 -4.051632 0.000000 -2.163468 -5.273603 2.921730 4.402578 ... 1.489058 -2.785946 -1.963624 75.0 M disease tissue stroke 1 1 1.0

10 rows × 100008 columns

数据预处理和数据类型转化已经完成，随后进行特征选择。首先使用PCA算法计算保留95%和99%信息的维度，并将数据降维至3维可视化数据分布。对甲基化位点采用方差过滤，高相关过滤和lasso回归.

In [2]:
#采用PCA算法计算保留95%和99%信息的weidu
Methylation=Data.iloc[:,1:-7]
Methylation.shape
Out[2]:
(6413, 100000)
In [ ]:
#执行PCA算法-计算保留99%信息时的维度
from sklearn.decomposition import PCA
Methylaion_PCA=PCA(n_components=0.99)
PCA_Methylation_99=Methylaion_PCA.fit_transform(Methylation)    #数据降维
PCA_Methylation_99.shape
In [4]:
#计算可解释方差比
import numpy as np
import matplotlib.pyplot as plt
Explain_variance=np.cumsum(Methylaion_PCA.explained_variance_ratio_)
#绘图
Explain_variance_plt=plt.figure(dpi=300)
Epl_variance=Explain_variance_plt.add_subplot(111)
Epl_variance.set_title('Explainable variance ratio curve')
Epl_variance.grid(color='black',linestyle='-.',alpha=0.2)
Epl_variance.plot(np.arange(1,len(Explain_variance)+1,1,dtype=int),Explain_variance,color='orange',linestyle='-')
#添加95%可解释方差曲线
Epl_variance.scatter(x=2500,y=0.95,color='green',marker='o')
Epl_variance.axhline(y=0.95, color='green', linestyle='-.')
Epl_variance.axvline(x=2500, color='green', linestyle='-.', label='Retain 95 per cent of explainable variance')
#添加99%可解释方差曲线
Epl_variance.scatter(3973,0.99,color='red',marker='o')
Epl_variance.axhline(y=0.99, color='red', linestyle='-.')
Epl_variance.axvline(x=3973, color='red', linestyle='-.', label='Retain 99 per cent of explainable variance')
Epl_variance.set_xlabel('n_components')
Epl_variance.set_ylabel('explained_variance_ratio')
plt.legend()
plt.show()
In [5]:
#使用PCA将数据降维至3维
from sklearn.decomposition import PCA
from pandas import DataFrame
PCADisplay3D=PCA(n_components=3)
Methylation3D_PCA=PCADisplay3D.fit_transform(Methylation)    #PCA3D降维
Methylation3D_PCA=DataFrame(Methylation)
Methylation3D_PCA['Disease_Encoder']=Data.loc[:,'disease_encode']
In [6]:
#绘制3D散点图
import matplotlib.pyplot as plt  
from mpl_toolkits.mplot3d import Axes3D
NormalData=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==0,:]
Stroke_Data=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==1,:]
OA_X,OA_Y,OA_Z=NormalData.iloc[:,0],NormalData.iloc[:,1],NormalData.iloc[:,2]
CON_X,CON_Y,CON_Z=Stroke_Data.iloc[:,0],Stroke_Data.iloc[:,1],Stroke_Data.iloc[:,2]
# 创建3D图形对象
fig=plt.figure(dpi=300)  
ax=fig.add_subplot(111, projection='3d')
ax.scatter(CON_X,CON_Y,CON_Z,color='green',label='Stroke')
ax.scatter(OA_X,OA_Y,OA_Z,color='orange',label='Normal')
# 设置标签  
ax.set_xlabel('Faeture 0')  
ax.set_ylabel('Feature 1')  
ax.set_zlabel('Feature 2')
plt.legend()
# 绘制投影到XY平面  
plt.figure() 
plt.title('XY plane projection')
plt.scatter(OA_X,OA_Y,color='orange',label='Normal')  
plt.scatter(CON_X,CON_Y,color='green',label='Stroke')  
plt.xlabel('Feature 0')  
plt.ylabel('Feature 1') 
plt.legend()
# 绘制投影到XZ平面  
plt.figure() 
plt.title('XZ plane projection')
plt.scatter(OA_X,OA_Z,color='orange',label='Normal')  
plt.scatter(CON_X,CON_Z,color='green',label='Stroke')  
plt.xlabel('Feature 0')  
plt.ylabel('Feature 2')
plt.legend()
# 绘制投影到YZ平面  
plt.figure() 
plt.title('YZ plane projection')
plt.scatter(OA_Y,OA_Z,color='orange',label='Normal')  
plt.scatter(CON_Y,CON_Z,color='green',label='Stroke')  
plt.xlabel('Feature 1')  
plt.ylabel('Feature 2')
plt.legend()
plt.show()
In [3]:
#对数据采取低方差过滤算法
from pandas import DataFrame
from sklearn.feature_selection import VarianceThreshold
Variance_Selector=VarianceThreshold(threshold=0.5)
VarSele_Data=Variance_Selector.fit_transform(Methylation)    #执行低方差过滤
VarSele_Data=DataFrame(VarSele_Data)
VarSele_Data.columns=Variance_Selector.get_feature_names_out()
VarSele_Data.head(10)
Out[3]:
cg00050873 cg00212031 cg00214611 cg01707559 cg02004872 cg02011394 cg02050847 cg02233190 cg02494853 cg02839557 ... cg12700439 cg12711755 cg12712409 cg12719753 cg12725420 cg12727528 cg12732791 cg12737588 cg12766407 cg12848808
0 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 -6.163916 0.000000 ... 2.715111 2.921730 1.417454 2.804368 -4.112908 -5.773449 1.152395 5.773449 -5.492861 -1.991711
1 2.049755 -3.472874 -3.837361 -2.375206 -3.659672 3.790034 4.690541 -3.701353 -3.406479 -3.886935 ... 2.375206 3.619579 2.241551 2.600812 -2.349789 -4.051632 0.909791 4.489850 -4.690541 -1.642924
2 2.196336 -2.985388 -2.963760 -2.324893 -3.790034 3.790034 4.247583 -3.701353 -3.790034 -2.901295 ... 2.985388 2.324893 2.049755 2.100192 -1.848299 -4.178048 1.045719 4.690541 -5.273603 -2.059685
3 0.000000 0.000000 0.000000 -1.242189 0.000000 0.000000 0.000000 0.000000 -4.322159 0.000000 ... 2.570129 2.388119 2.010774 2.300497 -2.264792 -3.993781 1.423834 4.402578 -5.093549 -2.648415
4 2.632328 -3.284902 -3.938986 -2.616463 -3.886935 2.942546 4.247583 -4.585271 -3.837361 -2.525567 ... 2.388119 3.659672 1.695385 2.585369 -2.059685 -4.322159 1.848299 5.492861 -4.585271 -2.681278
5 2.264792 -2.942546 -3.052934 -1.972920 -3.659672 3.543689 4.489850 -2.540233 -2.823096 -2.985388 ... 3.029956 3.507691 2.349789 2.230092 -2.069693 -4.690541 1.152395 4.807960 -5.773449 -1.991711
6 0.000000 0.000000 0.000000 -1.098346 0.000000 0.000000 0.000000 0.000000 -4.178048 0.000000 ... 2.732410 3.284902 1.814692 2.264792 -2.230092 -4.402578 1.823018 4.690541 -5.273603 -2.120936
7 1.963624 -4.178048 -4.178048 -2.324893 -5.492861 3.076403 4.940737 -2.401172 -3.619579 -3.149987 ... 2.555085 3.619579 1.790011 2.555085 -1.848299 -4.402578 1.300641 5.273603 -5.093549 -1.954393
8 1.874054 0.000000 0.000000 -1.798190 -4.112908 3.993781 4.585271 -3.659672 -3.314031 -3.149987 ... 2.767818 3.472874 1.522701 2.570129 -1.781880 -4.489850 0.949208 4.940737 -5.273603 -2.131436
9 1.462650 -3.256540 -4.051632 -2.163468 -5.273603 2.921730 4.402578 -2.511081 -2.921730 -2.942546 ... 2.767818 2.555085 1.945225 3.149987 -1.404768 -6.163916 0.000000 4.489850 -5.773449 -1.963624

10 rows × 63575 columns

In [6]:
#构建特征方差数据表
VarianseData=DataFrame()
VarianseData['MetaBolite']=Variance_Selector.feature_names_in_
VarianseData['Variances']=Variance_Selector.variances_
VarianseData.shape
Out[6]:
(100000, 2)
In [9]:
#输出甲基化位点方差值
VarianseData.to_excel('/mnt/workspace/Run_Result/Vraince_result.xlsx')
In [4]:
#执行基于F分布的方差分析进行高相关过滤
from sklearn.feature_selection import SelectKBest, f_classif
Selectk=SelectKBest(score_func=f_classif,k=3973)
FselectorData=Selectk.fit_transform(VarSele_Data,Data.loc[:,'disease_encode'])
FselectorData=pd.DataFrame(FselectorData)
feature_nameindex=Selectk.get_support(indices=True)    #获取特征索引
feature_names=VarSele_Data.columns    #获取特征名
Kfeature_names=[feature_names[i] for i in feature_nameindex]
FselectorData.columns=Kfeature_names
FselectorData.head(10)
Out[4]:
cg01404988 cg02393514 cg03315431 cg04065558 cg04294190 cg04490290 cg04630982 cg04667267 cg04931939 cg05032098 ... cg11183227 cg11353598 cg11540553 cg12031275 cg12160578 cg12309653 cg12368188 cg12379383 cg12414181 cg12627870
0 -1.536363 -1.557086 1.146922 -4.807960 1.695385 -1.141464 -0.132166 -0.934347 -2.785946 -0.540693 ... 0.739857 -1.179996 4.112908 -0.566575 -2.142023 -4.112908 -3.938986 -2.388119 -2.664729 -3.100385
1 -2.496772 -2.241551 2.152700 -4.489850 0.000000 0.000000 0.000000 0.000000 0.000000 -1.790011 ... 1.354970 -2.218738 3.701353 -1.522701 -1.954393 -3.076403 -3.886935 -1.749816 -2.362431 -2.749976
2 -2.482634 -1.628272 2.288480 -3.993781 0.204671 -2.468664 -1.680203 -2.152700 -2.901295 -2.001208 ... 1.536363 -2.163468 3.790034 0.188519 -2.120936 -2.901295 -3.374769 -1.710727 -3.701353 -2.616463
3 -3.149987 -1.954393 1.114405 -4.112908 2.089945 -0.188519 -0.949208 -1.174443 -3.256540 -0.851873 ... 2.100192 -2.454856 3.406479 -1.430239 -2.881228 -3.543689 -3.886935 -2.362431 -3.938986 -2.454856
4 -2.921730 -2.511081 2.525567 -3.993781 1.592260 -2.337277 -1.856830 -1.672671 -3.228904 -2.767818 ... 1.404768 -2.010774 3.886935 0.823416 -2.253116 -3.007447 -3.439160 -1.814692 -3.343970 -2.414368
5 -2.600812 -1.650305 2.616463 -4.051632 1.765757 -3.201956 -1.657722 -1.650305 -2.823096 -1.991711 ... 2.441207 -1.219330 4.489850 -0.216802 -2.648415 -2.732410 -3.314031 -1.564056 -1.991711 -2.732410
6 -2.861514 -0.823416 0.989323 -3.507691 2.030118 -0.347383 -0.100063 0.472506 -2.881228 -1.185564 ... 1.798190 -2.349789 3.837361 3.580953 -2.842141 -4.112908 -3.837361 -2.496772 -2.312634 -2.648415
7 -3.052934 -2.454856 2.230092 -3.439160 -1.294708 0.000000 -2.100192 0.000000 -1.578093 -2.362431 ... 1.430239 -2.585369 3.374769 4.178048 -1.814692 -2.414368 -3.886935 -2.767818 -2.312634 -2.482634
8 -2.496772 -2.427712 2.276579 -3.175661 -0.964166 0.000000 -1.695385 0.000000 -2.375206 -2.648415 ... 1.449607 -2.375206 3.659672 -1.806416 -2.079779 -2.414368 -3.580953 -2.441207 -2.842141 -2.715111
9 -2.525567 -2.600812 2.616463 -3.284902 -1.271175 0.000000 0.000000 0.000000 0.000000 -1.918093 ... 1.152395 -1.635580 3.507691 0.000000 -1.606561 -2.100192 -3.029956 -2.100192 -2.069693 -2.616463

10 rows × 3973 columns

In [8]:
#获取相关信息
KBestF_info=DataFrame()
KBestF_info['Feature']=Kfeature_names    #载入特征
KBestF_info['scores']=Selectk.scores_[:3973]
KBestF_info['P value']=Selectk.pvalues_[:3973]
KBestF_info.shape
Out[8]:
(3973, 3)
In [9]:
#输出相关计算结果
KBestF_info.to_excel('/mnt/workspace/Run_Result/FCorr_With_Methylation.xlsx')
In [5]:
#采用Lasso算法进行特征选择
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
Target=Data.loc[:,'disease_encode']
# 数据标准化  
scaler=StandardScaler()  
X_scaled=scaler.fit_transform(FselectorData)
# 拆分数据集  
X_train,X_test,y_train,y_test=train_test_split(X_scaled,Target, test_size=0.3, random_state=2024)
lasso_model = LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)    # 设置Lasso模型 
lasso_model.fit(X_train,y_train)    # 训练模型
Out[5]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In [6]:
#评估Lasso模型性能
print('Lasso模型训练集Accuracy为:',accuracy_score(y_train,lasso_model.predict(X_train)))
print('Lasso模型测试集Accuracy为:',accuracy_score(y_test,lasso_model.predict(X_test)))

Lasso模型训练集Accuracy为: 1.0
Lasso模型测试集Accuracy为: 0.9994802494802495
In [10]:
#查看模型混淆矩阵
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [11]:
#查看训练集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_train,target=y_train)
In [12]:
#查看测试集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_test,target=y_test)
In [13]:
import joblib
joblib.dump(lasso_model,'/mnt/workspace/Models/Lasso.pkl')
Out[13]:
['/mnt/workspace/Models/Lasso.pkl']
In [15]:
#提取特征并进行特征选择
import numpy as np
LassoFeature_Index=np.where(lasso_model.coef_!=0)[1]
LassoFeature=FselectorData.iloc[:,LassoFeature_Index]
LassoFeature.shape
Out[15]:
(6413, 70)
In [16]:
import matplotlib.pyplot as plt
Coef=plt.figure(dpi=300)
Coef_ax=Coef.add_subplot(111)
Coef_ax.grid(color='black',linestyle='-.',alpha=0.2)
Coef_ax.plot(LassoFeature_Index,pd.DataFrame(lasso_model.coef_[0][LassoFeature_Index]))
Coef_ax.set_xlabel('CG_index')
Coef_ax.set_ylabel('Coef')
Coef_ax.set_xticklabels(LassoFeature_Index)
plt.show()

/tmp/ipykernel_10044/197567409.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator.
  Coef_ax.set_xticklabels(LassoFeature_Index)

使用Lasso验证DNA甲基化位点特征选择后的结果

In [17]:
#使用特征选择后的模型验证模型精度-输入[batchs,70],输出[batch,1]
X_TrainEv,X_testEv,y_trainEv,y_testEv=train_test_split(LassoFeature,Target, test_size=0.3, random_state=2024)
lasso_modelEvaluateD=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)
lasso_modelEvaluateD.fit(X_TrainEv,y_trainEv)
Out[17]:
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook.
On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
                   solver='liblinear')
In [18]:
#训练集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_TrainEv,y_trainEv)
In [19]:
#测试集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_testEv,y_testEv)
In [20]:
#使用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold,cross_val_score
from sklearn.model_selection import cross_validate
def Model_Evelate_CV(Model,Data,Target):
    cv=KFold(n_splits=10, shuffle=True, random_state=2024)
    kv_scores =cross_validate(Model,Data,Target,cv=cv,scoring='accuracy',
                          return_train_score=True)
    print('Fit_time:',kv_scores['fit_time'])
    print('Mean Fit_time:',kv_scores['fit_time'].mean())
    print('score_time:',kv_scores['score_time'])
    print('Mean score_time:',kv_scores['score_time'].mean())
    print('train_score:',kv_scores['train_score'])
    print('Mean train_score:',kv_scores['train_score'].mean())
    print('test_score:',kv_scores['test_score'])
    print('Mean test_score:',kv_scores['test_score'].mean())
#用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
lasso_modelEvaluate=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)    # 设置Lasso模型
Model_Evelate_CV(Model=lasso_modelEvaluate,Data=LassoFeature,Target=Target)

Fit_time: [0.15090775 0.20123768 0.1979394  0.19683361 0.19801974 0.19604516
 0.19239807 0.17037344 0.16579437 0.18889141]
Mean Fit_time: 0.1858440637588501
score_time: [0.00298166 0.01086545 0.00288725 0.00284386 0.00290656 0.00284505
 0.00278687 0.00281239 0.00273204 0.00283217]
Mean score_time: 0.003649330139160156
train_score: [0.99930688 0.9991336  0.99930688 0.9989605  0.99948025 0.999307
 0.99948025 0.99913375 0.99948025 0.99913375]
Mean train_score: 0.9992723102452785
test_score: [0.99844237 0.99844237 1.         0.99843994 0.99687988 0.99843994
 1.         0.99687988 0.99843994 0.99687988]
Mean test_score: 0.9982844173580029
In [21]:
#整合并输出特征选择结果
CG_Feature=Data.iloc[:,LassoFeature_Index]
DNA_Methylstion_Feature=pd.concat([CG_Feature,Data.iloc[:,-7:]],axis=1)
DNA_Methylstion_Feature.shape
Out[21]:
(6413, 77)
In [22]:
DNA_Methylstion_Feature.to_excel('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.xlsx','UTF-8')
In [23]:
DNA_Methylstion_Feature.to_csv('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.csv')
In [24]:
joblib.dump(lasso_modelEvaluateD,'/mnt/workspace/Models/lasso_modelEvaluate.pkl')
Out[24]:
['/mnt/workspace/Models/lasso_modelEvaluate.pkl']

经过低方差过滤，基于F分布的相关分析和Lasso回归，获得了70个甲基化位点作为特征子集。

In [3]:
#读取DNA甲基化位点数据
import pandas as pd
MethylationData=pd.read_excel('Data/DNA_Methylstion_Feature.xlsx')
MethylationData.head(10)
Out[3]:
Unnamed: 0 cg03706273 cg00412368 cg01370179 cg01522249 cg01999212 cg03718079 cg03977822 cg05100261 cg05876899 ... cg01446576 cg01550055 cg01717973 age gender sample_type disease disease_encode sample_type_encode gender_encode
0 8040 -4.112908 -0.015997 -1.557086 0.372163 -2.525567 -0.851873 -0.124134 -0.832866 2.921730 ... -2.570129 -1.585160 -3.052934 78.0 F disease tissue stroke 1 1 0.0
1 8041 -2.767818 -1.417454 -1.417454 0.000000 -4.585271 -2.648415 -0.489446 -3.938986 3.228904 ... -3.744756 -1.247949 -3.886935 52.0 M disease tissue stroke 1 1 1.0
2 8042 -3.374769 -2.020410 -1.529517 1.650305 -4.112908 -2.362431 -0.430440 -4.940737 3.076403 ... -4.051632 -1.536363 -5.273603 92.0 M disease tissue stroke 1 1 1.0
3 8043 -4.807960 -0.339147 -1.814692 -0.744433 -2.985388 -1.798190 0.298130 -0.372163 3.374769 ... -4.585271 -1.757764 -4.178048 87.0 F disease tissue stroke 1 1 0.0
4 8044 -4.402578 -2.555085 -1.665178 1.734052 -3.507691 -3.149987 -0.128149 -4.247583 3.228904 ... -4.247583 -1.726235 -3.993781 87.0 M disease tissue stroke 1 1 1.0
5 8045 -3.149987 -1.831393 -1.599394 1.443126 -2.732410 -3.175661 -0.076021 -4.112908 3.314031 ... -4.112908 -1.253727 -4.112908 41.0 M disease tissue stroke 1 1 1.0
6 8046 -3.619579 -0.112095 -1.119787 -0.076021 -2.823096 -1.982282 0.164336 -0.019997 3.124904 ... -4.585271 -1.271175 -3.837361 78.0 F disease tissue stroke 1 1 0.0
7 8047 -4.051632 -2.079779 -1.550147 0.000000 -4.402578 -3.837361 -0.343263 -4.807960 3.284902 ... -4.322159 -1.324576 -4.051632 80.0 M disease tissue stroke 1 1 1.0
8 8048 -3.076403 -1.781880 -1.456115 2.241551 -3.837361 -2.441207 -0.285874 -5.492861 3.201956 ... -4.247583 -1.157883 -4.322159 69.0 M disease tissue stroke 1 1 1.0
9 8049 -2.664729 -1.718460 -1.392179 2.131436 -3.701353 -2.414368 -0.056003 -5.492861 3.543689 ... -3.659672 -0.974195 -3.886935 75.0 M disease tissue stroke 1 1 1.0

10 rows × 78 columns

统计和处理空缺数据

In [4]:
#统计甲基化数据
pd.isnull(MethylationData.iloc[:,:-7]).sum().sum()
Out[4]:
0
In [5]:
#统计其他附加数据
pd.isnull(MethylationData.iloc[:,-7:]).sum()
Out[5]:
age                    0
gender                68
sample_type            0
disease                0
disease_encode         0
sample_type_encode     0
gender_encode         68
dtype: int64
In [6]:
from collections import Counter
print(Counter(MethylationData['gender']))

Counter({'F': 3387, 'M': 2958, nan: 68})
In [7]:
MethylationData['gender'].fillna('M',inplace=True)
MethylationData['gender_encode'].fillna('1',inplace=True)
In [8]:
MethylationData['gender'].hist()
Out[8]:
<AxesSubplot:>
In [9]:
print(Counter(MethylationData['gender']))

Counter({'F': 3387, 'M': 3026})
In [10]:
pd.isnull(MethylationData).sum().sum()
Out[10]:
0

提取算法可处理的数据进行机器学习分析，筛选特征甲基化位点

In [11]:
Methylation=MethylationData.iloc[:,1:-7]    #甲基化数据
MapData=MethylationData.loc[:,['age','gender_encode','disease_encode']]    #其他附加数据
MLData=pd.concat([Methylation,MapData],axis=1)    #数据合并
MLData.shape
Out[11]:
(6413, 73)

由于正常样本与卒中样本总量差异较大，因此使用数据重采样算法解决数据类别分布不均衡问题。 数据重采样策略针对训练集进行，外部测试集不使用数据重采样。同时，数据重采样后的训练集采用10-折交叉验证训练，独立外部测试集测试。

In [12]:
#统计和汇总疾病数据分布
print(Counter(MethylationData['disease']))
MethylationData['disease'].hist()

Counter({'control': 6266, 'stroke': 147})
Out[12]:
<AxesSubplot:>

编写模型训练函数——常规训练

In [13]:
#查看模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [14]:
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def PR_Curve(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024)    #划分数据集
    predict_score=Model.predict_proba(X_test)[:, 1]    #获取概率值
    predict=Model.predict(X_test)     #获取预测标签
    accuracy=accuracy_score(y_test,predict)
    precision, recall, thresholds = precision_recall_curve(y_test, predict_score)    #计算PR曲线
    PR=DataFrame()    #将PR曲线数据合并到DataFrame
    #PR['thresholds']=thresholds
    PR['recall']=recall
    PR['precision']=precision
    return PR,accuracy
In [15]:
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def ROC_Curve(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024)    #划分数据集
    predict_score=Model.predict_proba(X_test)[:, 1]    #获取概率值
    fpr, tpr, thresholds = roc_curve(y_test, predict_score)
    roc_auc = auc(fpr, tpr)    #计算AUC
    ROC=DataFrame()    #将PR曲线数据合并到DataFrame
    #ROC['thresholds']=thresholds
    ROC['tpr']=tpr
    ROC['fpr']=fpr
    return ROC,roc_auc
In [16]:
#编写模型训练评估函数
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTestModel(Model,Data,Label):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024)    #划分数据集
    Model.fit(X_train,y_train)    #训练模型
    TrainPredict=Model.predict(X_train)    #计算训练集指标
    TrainPrecision=precision_score(y_train,TrainPredict)
    TrainRecall=recall_score(y_train,TrainPredict)
    TrainF1=f1_score(y_train,TrainPredict)
    TrainAcuracy=accuracy_score(y_train,TrainPredict)
    print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(TrainPrecision,TrainRecall,TrainF1,TrainAcuracy))
    TestPredict=Model.predict(X_test)    #测试集预测结果
    TestPrecision=precision_score(y_test,TestPredict)
    TestRecall=recall_score(y_test,TestPredict)
    TestF1=f1_score(y_test,TestPredict)
    TestAccuracy=accuracy_score(y_test,TestPredict)
    print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
    print('-------------------测试集混淆举证-------------------')
    Display_ConfusionMatrix(model=Model,data=X_test,target=y_test)

编写模型训练函数——数据重采样

In [17]:
#绘制模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Computer_ConfusionMatrix(model,data,target):
    ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1])    #计算混淆举证
    Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
    Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
    plt.title('Confusion Matrix')
    plt.show()
In [18]:
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def Computer_PRCurve(Model,Data,Label):
    predict_score=Model.predict_proba(Data)[:, 1]    #获取概率值
    predict=Model.predict(Data)     #获取预测标签
    accuracy=accuracy_score(Label,predict)
    precision, recall, thresholds = precision_recall_curve(Label, predict_score)    #计算PR曲线
    PR=DataFrame()    #将PR曲线数据合并到DataFrame
    PR['recall']=recall
    PR['precision']=precision
    return PR,accuracy
In [19]:
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def Computer_ROCCurve(Model,Data,Label):
    predict_score=Model.predict_proba(Data)[:, 1]    #获取概率值
    fpr, tpr, thresholds = roc_curve(Label, predict_score)
    roc_auc = auc(fpr, tpr)    #计算AUC
    ROC=DataFrame()    #将PR曲线数据合并到DataFrame
    ROC['tpr']=tpr
    ROC['fpr']=fpr
    return ROC,roc_auc
In [20]:
#模型训练-评估函数
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split,KFold
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTrainModelCV(Resmaple,Model,Data,Label,cv):
    X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024)    #划分数据集
    X_train_Re,y_train_Re=Resmaple.fit_resample(X_train,y_train)
    print('resample shape:',Counter(y_train_Re))
    kf=KFold(n_splits=cv, shuffle=True, random_state=2025)
    Precision_fold,Recall_fold,F1Score_fold,Accuracy_fold=[],[],[],[]
    ValidPrecision_fold,ValidRecall_fold,ValidF1Score_fold,ValidAccuracy_fold=[],[],[],[]
    for train_index,valid_index in kf.split(X_train_Re):
        # 划分出当前折的训练和验证数据
        X_fold_train, X_fold_val = X_train_Re.iloc[train_index,:], X_train_Re.iloc[valid_index,:]
        y_fold_train, y_fold_val = y_train_Re.iloc[train_index], y_train_Re.iloc[valid_index]
        Model.fit(X_fold_train,y_fold_train)    #训练模型
        # Train Score
        TrainPredict_fold=Model.predict(X_fold_train)    #计算训练集指标
        TrainPrecision=precision_score(y_fold_train,TrainPredict_fold)
        Precision_fold.append(TrainPrecision)
        TrainRecall=recall_score(y_fold_train,TrainPredict_fold)
        Recall_fold.append(TrainRecall)
        TrainF1=f1_score(y_fold_train,TrainPredict_fold)
        F1Score_fold.append(TrainF1)
        TrainAcuracy=accuracy_score(y_fold_train,TrainPredict_fold)
        Accuracy_fold.append(TrainAcuracy)
        # Valid Score
        ValidPredict_fold=Model.predict(X_fold_val)    #计算训练集指标
        ValidPrecision=precision_score(y_fold_val,ValidPredict_fold)
        ValidPrecision_fold.append(ValidPrecision)
        ValidRecall=recall_score(y_fold_val,ValidPredict_fold)
        ValidRecall_fold.append(ValidRecall)
        ValidF1=f1_score(y_fold_val,ValidPredict_fold)
        ValidF1Score_fold.append(ValidF1)
        ValidAcuracy=accuracy_score(y_fold_val,ValidPredict_fold)
        ValidAccuracy_fold.append(ValidAcuracy)
    print('Model train Precision of flod:',Precision_fold)
    print('Model train Recall of flod:',Recall_fold)
    print('Model train F1 Score of flod:',F1Score_fold)
    print('Model train Accuracy of flod:',Accuracy_fold)
    print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(Precision_fold),np.mean(Recall_fold),
                                                                           np.mean(F1Score_fold),np.mean(Accuracy_fold)))
    print('Model valid Precision of flod:',ValidPrecision_fold)
    print('Model valid train Recall of flod:',ValidRecall_fold)
    print('Model valid train F1 Score of flod:',ValidF1Score_fold)
    print('Model valid train Accuracy of flod:',ValidAccuracy_fold)
    print('模型Valid集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(ValidPrecision_fold),np.mean(ValidRecall_fold),
                                                                           np.mean(ValidF1Score_fold),np.mean(ValidAccuracy_fold)))
    Model.fit(X_train_Re,y_train_Re)
    TestPredict=Model.predict(X_test)    #测试集预测结果
    TestPrecision=precision_score(y_test,TestPredict)
    TestRecall=recall_score(y_test,TestPredict)
    TestF1=f1_score(y_test,TestPredict)
    TestAccuracy=accuracy_score(y_test,TestPredict)
    print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
    print('-------------------测试集混淆举证-------------------')
    Computer_ConfusionMatrix(model=Model,data=X_test,target=y_test)
    PR,accuracy=Computer_PRCurve(Model=Model,Data=X_test,Label=y_test)
    ROC,roc_auc=Computer_ROCCurve(Model=Model,Data=X_test,Label=y_test)
    return PR,accuracy,ROC,roc_auc,Model

划分数据集并测试欠采样算法.

In [21]:
#切割数据
MLTestData=MLData.iloc[:,:-1]
MLTestLabel=MLData.iloc[:,-1]
In [22]:
#数据归一化
from pandas import DataFrame
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ColumnsNames=MLTestData.columns
Stand=StandardScaler()
StandData=Stand.fit_transform(MLTestData)
MinMax=MinMaxScaler(feature_range=(0,1))
MinMaxData=MinMax.fit_transform(StandData)
MLTestData=DataFrame(MinMaxData)
MLTestData.columns=ColumnsNames
In [23]:
#使用随机森林算法作为衡量基准测试数据重采样算法性能
from sklearn.ensemble import RandomForestClassifier
Forest=RandomForestClassifier(random_state=2024)
RunTestModel(Model=Forest,Data=MLTestData,Label=MLTestLabel)

模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792
-------------------测试集混淆举证-------------------
In [24]:
#计算PR和ROC曲线
Forest_PR,Forest_Accuracy=PR_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_ROC,Forest_ROC_AUC=ROC_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_PR.to_excel('Data/PR Curve/Resample/Forest_PR.xlsx')
Forest_ROC.to_excel('Data/ROC Curve/Resample/Forest_ROC.xlsx')
In [25]:
#使用CNN算法进行欠采样
from imblearn.under_sampling import CondensedNearestNeighbour
CNN=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestCNN=RandomForestClassifier(random_state=2024)
ForestCNN_PR,accuracyCNN,ForestCNN_ROC,roc_auc_CNN,Forestcnn=RunTrainModelCV(Resmaple=CNN,Model=ForestCNN,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 0.8181818181818182, 0.8888888888888888, 1.0, 1.0, 1.0, 0.8, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.875, 1.0, 0.8888888888888888, 0.7272727272727273, 0.6363636363636364, 0.8181818181818182, 0.7272727272727273, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.8235294117647058, 0.9333333333333333, 0.9, 0.8888888888888888, 0.8421052631578948, 0.7777777777777778, 0.9, 0.761904761904762, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.896551724137931, 0.9655172413793104, 0.9310344827586207, 0.9285714285714286, 0.8928571428571429, 0.8571428571428571, 0.9285714285714286, 0.8214285714285714, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9507070707070706,Recall:0.7683585858585859,F1_Score:0.8406486805248414,Accuracy:0.8935960591133005
模型测试集Precision:0.782608695652174,Recall:0.8181818181818182,F1_score:0.8,Accuracy:0.9906444906444907
-------------------测试集混淆举证-------------------
In [26]:
#输出PR和ROC曲线数据
ForestCNN_PR.to_excel('Data/PR Curve/Resample/ForestCNN_PR.xlsx')
ForestCNN_ROC.to_excel('Data/ROC Curve/Resample/ForestCNN_ROC.xlsx')
In [27]:
#使用IHT算法进行欠采样
from imblearn.under_sampling import InstanceHardnessThreshold
IHT=InstanceHardnessThreshold(random_state=2024,cv=5,n_jobs=-1)
ForestIHT=RandomForestClassifier(random_state=2024)
ForestIHT_PR,accuracyIHT,ForestIHT_ROC,roc_auc_IHT,ForestIHT=RunTrainModelCV(Resmaple=IHT,Model=ForestIHT,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 2265, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.3333333333333333, 0.7142857142857143, 0.45454545454545453, 0.8333333333333334, 0.5, 0.375, 0.5384615384615384, 0.7142857142857143, 0.38461538461538464, 0.6363636363636364]
Model valid train F1 Score of flod: [0.5, 0.8333333333333333, 0.625, 0.9090909090909091, 0.6666666666666666, 0.5454545454545454, 0.7000000000000001, 0.8333333333333333, 0.5555555555555556, 0.7777777777777778]
Model valid train Accuracy of flod: [0.9831223628691983, 0.9915611814345991, 0.9746835443037974, 0.9915611814345991, 0.9831223628691983, 0.9789029535864979, 0.9746835443037974, 0.9831223628691983, 0.9661016949152542, 0.9830508474576272]
模型Valid集Precision:1.0,Recall:0.548422410922411,F1_Score:0.6946212121212121,Accuracy:0.9809912036043766
模型测试集Precision:0.7333333333333333,Recall:0.5,F1_score:0.5945945945945945,Accuracy:0.9844074844074844
-------------------测试集混淆举证-------------------
In [28]:
#计算PR和ROC曲线
ForestIHT_PR.to_excel('Data/PR Curve/Resample/ForestIHT_PR.xlsx')
ForestIHT_ROC.to_excel('Data/ROC Curve/Resample/ForestIHT_ROC.xlsx')
In [29]:
#使用NearMiss进行欠采样
from imblearn.under_sampling import NearMiss
NM=NearMiss(sampling_strategy='not minority',n_jobs=-1)
ForestNM=RandomForestClassifier(random_state=2024)
ForestNM_PR,accuracyNM,ForestNM_ROC,roc_auc_NM,ForestNM=RunTrainModelCV(Resmaple=NM,Model=ForestNM,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 0.9285714285714286, 1.0, 0.9, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.8888888888888888, 1.0, 0.9333333333333333, 0.9285714285714286, 0.75, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train F1 Score of flod: [0.9411764705882353, 1.0, 0.9655172413793104, 0.9285714285714286, 0.8571428571428571, 0.9473684210526316, 1.0, 1.0, 1.0, 1.0]
Model valid train Accuracy of flod: [0.9523809523809523, 1.0, 0.9523809523809523, 0.9047619047619048, 0.9047619047619048, 0.9523809523809523, 1.0, 1.0, 1.0, 1.0]
模型Valid集Precision:0.9828571428571429,Recall:0.950079365079365,F1_Score:0.9639776418734464,Accuracy:0.9666666666666666
模型测试集Precision:0.037374658158614404,Recall:0.9318181818181818,F1_score:0.07186678352322523,Accuracy:0.4495841995841996
-------------------测试集混淆举证-------------------
In [30]:
#计算PR和ROC曲线
ForestNM_PR.to_excel('Data/PR Curve/Resample/ForestNM_PR.xlsx')
ForestNM_ROC.to_excel('Data/ROC Curve/Resample/ForestNM_ROC.xlsx')
In [31]:
#使用NeighbourhoodCleaningRule进行欠采样
from imblearn.under_sampling import NeighbourhoodCleaningRule
NBC=NeighbourhoodCleaningRule(sampling_strategy='not minority',n_jobs=-1)
ForestNBC=RandomForestClassifier(random_state=2024)
ForestNBC_PR,accuracyNBC,ForestNBC_ROC,roc_auc_NBC,ForestNBC=RunTrainModelCV(Resmaple=NBC,Model=ForestNBC,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 4325, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.16666666666666666, 0.0, 0.0, 0.2222222222222222, 0.0, 0.0625, 0.09090909090909091, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.2857142857142857, 0.0, 0.0, 0.3636363636363636, 0.0, 0.11764705882352941, 0.16666666666666669, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9729119638826185, 0.9887133182844243, 0.9661399548532731, 0.981941309255079, 0.9841986455981941, 0.9796839729119639, 0.9661399548532731, 0.9774266365688488, 0.9796380090497737, 0.9819004524886877]
模型Valid集Precision:0.4,Recall:0.054229797979797975,F1_Score:0.09336643748408455,Accuracy:0.9778694217746138
模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792
-------------------测试集混淆举证-------------------
In [32]:
#计算PR和ROC曲线
ForestNBC_PR.to_excel('Data/PR Curve/Resample/ForestNBC_PR.xlsx')
ForestNBC_ROC.to_excel('Data/ROC Curve/Resample/ForestNBC_ROC.xlsx')
In [33]:
#使用OneSidedSelection进行欠采样
from imblearn.under_sampling import OneSidedSelection
OSS=OneSidedSelection(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestOSS=RandomForestClassifier(random_state=2024)
ForestOSS_PR,accuracyOSS,ForestOSS_ROC,roc_auc_OSS,ForestOSS=RunTrainModelCV(Resmaple=OSS,Model=ForestOSS,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 4345, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.25, 0.13333333333333333, 0.15384615384615385, 0.15384615384615385, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.4, 0.23529411764705882, 0.2666666666666667, 0.2666666666666667, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9752808988764045, 0.9865168539325843, 0.9707865168539326, 0.9752808988764045, 0.9752808988764045, 0.9887640449438202, 0.9775280898876404, 0.9730337078651685, 0.9864864864864865, 0.9774774774774775]
模型Valid集Precision:0.4,Recall:0.0691025641025641,F1_Score:0.11686274509803922,Accuracy:0.9786435874076321
模型测试集Precision:1.0,Recall:0.06818181818181818,F1_score:0.1276595744680851,Accuracy:0.9786902286902287
-------------------测试集混淆举证-------------------
In [34]:
#计算PR和ROC曲线
ForestOSS_PR.to_excel('Data/PR Curve/Resample/ForestOSS_PR.xlsx')
ForestOSS_ROC.to_excel('Data/ROC Curve/Resample/ForestOSS_ROC.xlsx')
In [35]:
#使用随机欠采样进行数据欠采样
from imblearn.under_sampling import RandomUnderSampler
RUS=RandomUnderSampler(sampling_strategy='not minority',random_state=2024)
ForestRUS=RandomForestClassifier(random_state=2024)
ForestRUS_PR,accuracyRUS,ForestRUS_ROC,roc_auc_RUS,ForestRUS=RunTrainModelCV(Resmaple=RUS,Model=ForestRUS,Data=MLTestData,Label=MLTestLabel,cv=10)

resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.9, 0.8181818181818182, 1.0, 1.0, 0.8, 1.0, 1.0, 0.9, 1.0, 1.0]
Model valid train Recall of flod: [1.0, 0.9, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888]
Model valid train F1 Score of flod: [0.9473684210526316, 0.8571428571428572, 0.9655172413793104, 1.0, 0.888888888888889, 1.0, 1.0, 0.9473684210526316, 1.0, 0.9411764705882353]
Model valid train Accuracy of flod: [0.9523809523809523, 0.8571428571428571, 0.9523809523809523, 1.0, 0.9047619047619048, 1.0, 1.0, 0.95, 1.0, 0.95]
模型Valid集Precision:0.9418181818181818,Recall:0.9722222222222221,F1_Score:0.9547462300104556,Accuracy:0.9566666666666667
模型测试集Precision:0.2679738562091503,Recall:0.9318181818181818,F1_score:0.416243654822335,Accuracy:0.9402286902286903
-------------------测试集混淆举证-------------------
In [36]:
#计算PR和ROC曲线
ForestRUS_PR.to_excel('Data/PR Curve/Resample/ForestRUS_PR.xlsx')
ForestRUS_ROC.to_excel('Data/ROC Curve/Resample/ForestRUS_ROC.xlsx')
In [37]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(Forest_PR['recall'], Forest_PR['precision'],color='red',label='RandomForest=%f'%Forest_Accuracy)
PR_ax.plot(ForestCNN_PR['recall'], ForestCNN_PR['precision'],color='green',label='CNN=%f'%accuracyCNN)
PR_ax.plot(ForestIHT_PR['recall'], ForestIHT_PR['precision'],color='blue',label='IHT=%f'%accuracyIHT)
PR_ax.plot(ForestNBC_PR['recall'], ForestNBC_PR['precision'],color='yellow',label='NBC=%f'%accuracyNBC)
PR_ax.plot(ForestNM_PR['recall'], ForestNM_PR['precision'],color='purple',label='NM=%f'%accuracyNM)
PR_ax.plot(ForestOSS_PR['recall'], ForestOSS_PR['precision'],color='cyan',label='OSS=%f'%accuracyOSS)
PR_ax.plot(ForestRUS_PR['recall'],ForestRUS_PR['precision'],color='pink',label='RUS=%f'%accuracyRUS)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')  
PR_ax.set_ylabel('Precision')    
plt.legend(loc="best")  
plt.show()
In [38]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(Forest_ROC['fpr'], Forest_ROC['tpr'],color='red',label='RandomForest=%f'%Forest_ROC_AUC)
ROC_ax.plot(ForestCNN_ROC['fpr'], ForestCNN_ROC['tpr'],color='green',label='CNN=%f'%roc_auc_CNN)
ROC_ax.plot(ForestIHT_ROC['fpr'], ForestIHT_ROC['tpr'],color='blue',label='IHT=%f'%roc_auc_IHT)
ROC_ax.plot(ForestNBC_ROC['fpr'], ForestNBC_ROC['tpr'],color='yellow',label='NBC=%f'%roc_auc_NBC)
ROC_ax.plot(ForestNM_ROC['fpr'], ForestNM_ROC['tpr'],color='purple',label='NM=%f'%roc_auc_NM)
ROC_ax.plot(ForestOSS_ROC['fpr'], ForestOSS_ROC['tpr'],color='cyan',label='OSS=%f'%roc_auc_OSS)
ROC_ax.plot(ForestRUS_ROC['fpr'],ForestRUS_ROC['tpr'],color='pink',label='RUS=%f'%roc_auc_RUS)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])  
ROC_ax.set_ylim([0, 1.05])  
ROC_ax.set_xlabel('FPR')  
ROC_ax.set_ylabel('TPR')    
plt.legend(loc="best")  
plt.show()
In [39]:
import joblib
Model=[Forest,ForestCNN,ForestIHT,ForestNM,ForestNBC,ForestOSS,ForestRUS]
ModelStr=['Forest.pkl','ForestCNN.pkl','ForestIHT.pkl','ForestNM.pkl','ForestNBC.pkl','ForestOSS.pkl','ForestRUS.pkl']
try:
    for model,modelstr in zip(Model,ModelStr):
        joblib.dump(model,'ML Models/Resample/'+modelstr)
    print('模型保存成功！')
except:
    print('模型保存异常！！！')

模型保存成功！

选择CNN作为数据重采样算法

In [40]:
ReData=MLData.iloc[:,:-1]    #提取数据集
ReLabel=MLData.iloc[:,-1]    #提取数据标签
print(ReData.shape)
print(ReLabel.shape) 

(6413, 72)
(6413,)

预处理数据：数据缩放、描述性统计学

In [41]:
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ReMLData_name=ReData.columns
MLStand=StandardScaler()    #数据标准化
StandData=MLStand.fit_transform(ReData)
MinMaxData=MinMaxScaler(feature_range=(0,1))    #数据缩放
MinMaxdata=MinMaxData.fit_transform(StandData)
MLRunData=pd.DataFrame(MinMaxdata)
MLRunData.columns=ReMLData_name
MLRunData.head(10)
Out[41]:
cg03706273 cg00412368 cg01370179 cg01522249 cg01999212 cg03718079 cg03977822 cg05100261 cg05876899 cg06104510 ... cg00941229 cg00958409 cg00962755 cg01077609 cg01101221 cg01446576 cg01550055 cg01717973 age gender_encode
0 0.366703 0.699228 0.627539 0.339860 0.725793 0.719028 0.610471 0.692839 0.495571 0.842822 ... 0.725793 0.715924 0.372852 0.399866 0.645909 0.720955 0.598192 0.541884 0.684211 0.0
1 0.463465 0.592648 0.638988 0.312063 0.502166 0.564485 0.531935 0.435958 0.520210 0.606743 ... 0.583368 0.699491 0.360245 0.375090 0.543283 0.593423 0.671892 0.468489 0.456140 1.0
2 0.419803 0.546794 0.629799 0.435326 0.553452 0.589086 0.544620 0.353111 0.507977 0.627347 ... 0.522001 0.742118 0.410469 0.375090 0.603949 0.560104 0.608857 0.346457 0.807018 1.0
3 0.316703 0.674653 0.606416 0.256460 0.675869 0.637623 0.701250 0.730939 0.531910 0.750907 ... 0.566385 0.722588 0.310634 0.399866 0.567367 0.502166 0.560468 0.442870 0.763158 0.0
4 0.345865 0.506133 0.618676 0.441581 0.619161 0.521338 0.609608 0.410436 0.520210 0.644800 ... 0.583368 0.740716 0.404357 0.349814 0.549814 0.538830 0.567359 0.459086 0.763158 1.0
5 0.435973 0.561169 0.624070 0.419851 0.703336 0.519129 0.620814 0.421574 0.527038 0.624162 ... 0.477988 0.757873 0.433043 0.382025 0.543283 0.553452 0.670629 0.448602 0.359649 1.0
6 0.402192 0.691920 0.663395 0.306385 0.693490 0.621787 0.672487 0.760064 0.511868 0.839015 ... 0.553452 0.705214 0.350654 0.295393 0.481720 0.502166 0.666816 0.472852 0.684211 0.0
7 0.371111 0.542279 0.628108 0.312063 0.522001 0.462208 0.563362 0.364092 0.524701 0.624162 ... 0.619161 0.701428 0.465032 0.359144 0.582520 0.530733 0.655144 0.453995 0.701754 1.0
8 0.441267 0.564934 0.635818 0.479486 0.583368 0.582309 0.575699 0.307450 0.518048 0.590573 ... 0.615253 0.712455 0.357063 0.388415 0.595861 0.538830 0.691576 0.430188 0.605263 1.0
9 0.470881 0.569757 0.641060 0.471262 0.598135 0.584618 0.625118 0.307450 0.545459 0.560855 ... 0.649430 0.724201 0.348719 0.382025 0.561819 0.602660 0.731723 0.468489 0.657895 1.0

10 rows × 72 columns

使用CNN算法进行预处理

In [42]:
from imblearn.under_sampling import CondensedNearestNeighbour
CNNML=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
In [43]:
#测试Logistic回归
from sklearn.linear_model import LogisticRegression
Logistic=LogisticRegression(penalty="l2",dual=True,tol=1e-4,C=1.0,fit_intercept=True,random_state=2024,solver='liblinear',
                            max_iter=100,multi_class="auto",verbose=0, warm_start=False,n_jobs=-1,l1_ratio=None)
Logistic_PR,Logistic_Accuracy,Logistic_ROC,Logistic_AUC,Logistic_ML=RunTrainModelCV(Resmaple=CNNML,Model=Logistic,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9365079365079365, 0.8970588235294118, 0.9130434782608695, 0.9130434782608695, 0.921875, 0.9333333333333333, 0.9508196721311475, 0.9230769230769231, 0.9344262295081968, 0.9142857142857143]
Model train Recall of flod: [0.6344086021505376, 0.6421052631578947, 0.6702127659574468, 0.6702127659574468, 0.6413043478260869, 0.6086956521739131, 0.6304347826086957, 0.6521739130434783, 0.6263736263736264, 0.6956521739130435]
Model train F1 Score of flod: [0.7564102564102564, 0.7484662576687117, 0.7730061349693251, 0.7730061349693251, 0.7564102564102563, 0.7368421052631579, 0.758169934640523, 0.7643312101910829, 0.7500000000000001, 0.7901234567901234]
Model train Accuracy of flod: [0.8503937007874016, 0.8385826771653543, 0.8543307086614174, 0.8549019607843137, 0.8509803921568627, 0.8431372549019608, 0.8549019607843137, 0.8549019607843137, 0.8509803921568627, 0.8666666666666667]
模型训练集Precision:0.9237470588894403,Recall:0.6471573893162169,F1_Score:0.7606765747312763,Accuracy:0.8519777674849467
Model valid Precision of flod: [1.0, 0.8888888888888888, 0.7142857142857143, 1.0, 0.8333333333333334, 1.0, 0.7777777777777778, 0.8888888888888888, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.5555555555555556, 0.6666666666666666, 0.45454545454545453, 0.7272727272727273, 0.6363636363636364, 0.7272727272727273, 0.5, 0.45454545454545453]
Model valid train F1 Score of flod: [0.5714285714285715, 0.9411764705882353, 0.6250000000000001, 0.8, 0.5882352941176471, 0.8421052631578948, 0.7000000000000001, 0.7999999999999999, 0.6666666666666666, 0.625]
Model valid train Accuracy of flod: [0.7931034482758621, 0.9655172413793104, 0.7931034482758621, 0.8928571428571429, 0.75, 0.8928571428571429, 0.7857142857142857, 0.8571428571428571, 0.7857142857142857, 0.7857142857142857]
模型Valid集Precision:0.9103174603174603,Recall:0.6122222222222222,F1_Score:0.7159612265959016,Accuracy:0.8301724137931034
模型测试集Precision:0.8064516129032258,Recall:0.5681818181818182,F1_score:0.6666666666666667,Accuracy:0.987006237006237
-------------------测试集混淆举证-------------------
In [44]:
#输出PR曲线和ROC曲线结果
Logistic_PR.to_excel('Data/PR Curve/ML/Logistic_PR.xlsx','UTF-8')
Logistic_ROC.to_excel('Data/ROC Curve/ML/Logistic_ROC.xlsx','UTF-8')
In [45]:
#测试SVM算法
from sklearn.svm import SVC
SVM_Classifier=SVC(C=1.0,kernel="rbf",degree=3,gamma="scale",coef0=0.0,shrinking=True,probability=True,tol=1e-3, 
                   cache_size=200,max_iter=-1, decision_function_shape="ovr",random_state=2024)
SVM_PR,SVM_Accuracy,SVM_ROC,SVM_AUC,SVM_ML=RunTrainModelCV(Resmaple=CNNML,Model=SVM_Classifier,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9473684210526315, 0.9487179487179487, 0.9285714285714286, 0.9302325581395349, 0.927710843373494, 0.935064935064935, 0.9466666666666667, 0.9397590361445783, 0.9333333333333333, 0.9156626506024096]
Model train Recall of flod: [0.7741935483870968, 0.7789473684210526, 0.8297872340425532, 0.851063829787234, 0.8369565217391305, 0.782608695652174, 0.7717391304347826, 0.8478260869565217, 0.7692307692307693, 0.8260869565217391]
Model train F1 Score of flod: [0.8520710059171597, 0.8554913294797688, 0.8764044943820225, 0.888888888888889, 0.8800000000000001, 0.8520710059171598, 0.8502994011976047, 0.8914285714285715, 0.8433734939759036, 0.8685714285714285]
Model train Accuracy of flod: [0.9015748031496063, 0.9015748031496063, 0.9133858267716536, 0.9215686274509803, 0.9176470588235294, 0.9019607843137255, 0.9019607843137255, 0.9254901960784314, 0.8980392156862745, 0.9098039215686274]
模型训练集Precision:0.9353087821666962,Recall:0.8068440141173052,F1_Score:0.8658599619758508,Accuracy:0.9093006021306161
Model valid Precision of flod: [0.8, 0.8888888888888888, 0.875, 0.7777777777777778, 1.0, 1.0, 0.9, 0.8181818181818182, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.7777777777777778, 0.7777777777777778, 0.5454545454545454, 0.6363636363636364, 0.8181818181818182, 0.8181818181818182, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.5333333333333333, 0.9411764705882353, 0.823529411764706, 0.7777777777777778, 0.7058823529411764, 0.7777777777777778, 0.8571428571428572, 0.8181818181818182, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.7586206896551724, 0.9655172413793104, 0.896551724137931, 0.8571428571428571, 0.8214285714285714, 0.8571428571428571, 0.8928571428571429, 0.8571428571428571, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9059848484848485,Recall:0.7084343434343434,F1_Score:0.7813749167928734,Accuracy:0.8620689655172413
模型测试集Precision:0.85,Recall:0.7727272727272727,F1_score:0.8095238095238095,Accuracy:0.9916839916839917
-------------------测试集混淆举证-------------------
In [46]:
#输出PR曲线和ROC曲线结果
SVM_PR.to_excel('Data/PR Curve/ML/SVM_PR.xlsx','UTF-8')
SVM_ROC.to_excel('Data/ROC Curve/ML/SVM_ROC.xlsx','UTF-8')
In [47]:
#测试决策树算法
import numpy as np
from sklearn.tree import DecisionTreeClassifier
DecisionTree=DecisionTreeClassifier(criterion='entropy',splitter='best',max_depth=7,min_samples_split=6,random_state=2024)
DTC_PR,DTC_Accuracy,DTC_ROC,DTC_AUC,DTC_ML=RunTrainModelCV(Resmaple=CNNML,Model=DecisionTree,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.989010989010989, 0.92, 0.9891304347826086, 0.9893617021276596, 0.9578947368421052, 0.9479166666666666, 0.9278350515463918, 0.989247311827957, 0.9574468085106383, 0.9574468085106383]
Model train Recall of flod: [0.967741935483871, 0.968421052631579, 0.9680851063829787, 0.9893617021276596, 0.9891304347826086, 0.9891304347826086, 0.9782608695652174, 1.0, 0.989010989010989, 0.9782608695652174]
Model train F1 Score of flod: [0.9782608695652174, 0.9435897435897437, 0.9784946236559139, 0.9893617021276596, 0.9732620320855614, 0.9680851063829786, 0.9523809523809524, 0.9945945945945946, 0.972972972972973, 0.967741935483871]
Model train Accuracy of flod: [0.984251968503937, 0.9566929133858267, 0.984251968503937, 0.9921568627450981, 0.9803921568627451, 0.9764705882352941, 0.9647058823529412, 0.996078431372549, 0.9803921568627451, 0.9764705882352941]
模型训练集Precision:0.9625290509825655,Recall:0.9817403394332729,F1_Score:0.9718744532839466,Accuracy:0.9791863517060367
Model valid Precision of flod: [0.8571428571428571, 0.6, 0.6363636363636364, 0.5, 0.8888888888888888, 0.8888888888888888, 0.75, 0.7272727272727273, 0.8888888888888888, 0.7272727272727273]
Model valid train Recall of flod: [0.6, 0.75, 0.7777777777777778, 0.4444444444444444, 0.7272727272727273, 0.7272727272727273, 0.5454545454545454, 0.7272727272727273, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.6666666666666665, 0.7000000000000001, 0.47058823529411764, 0.7999999999999999, 0.7999999999999999, 0.631578947368421, 0.7272727272727273, 0.761904761904762, 0.7272727272727273]
Model valid train Accuracy of flod: [0.8275862068965517, 0.7931034482758621, 0.7931034482758621, 0.6785714285714286, 0.8571428571428571, 0.8571428571428571, 0.75, 0.7857142857142857, 0.8214285714285714, 0.7857142857142857]
模型Valid集Precision:0.7464718614718614,Recall:0.6693434343434344,F1_Score:0.6991166418720598,Accuracy:0.7949507389162561
模型测试集Precision:0.1336206896551724,Recall:0.7045454545454546,F1_score:0.2246376811594203,Accuracy:0.8887733887733887
-------------------测试集混淆举证-------------------
In [48]:
#输出PR曲线和ROC曲线结果
DTC_PR.to_excel('Data/PR Curve/ML/DTC_PR.xlsx','UTF-8')
DTC_ROC.to_excel('Data/ROC Curve/ML/DTC_ROC.xlsx','UTF-8')
In [49]:
#对随机森林进行网格搜索
from sklearn.ensemble import RandomForestClassifier
RandomForest=RandomForestClassifier(n_estimators=27,criterion='entropy',max_depth=7,min_samples_split=6,
                                    bootstrap=True,min_samples_leaf=9,max_features='sqrt',random_state=2024)
RondomForest_PR,RondomForest_Accuracy,RondomForest_ROC,RondomForest_AUC,Forest_ML=RunTrainModelCV(Resmaple=CNNML,Model=RandomForest,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9887640449438202, 1.0, 0.9886363636363636, 1.0, 0.9886363636363636, 1.0, 1.0, 0.967032967032967, 1.0, 0.9882352941176471]
Model train Recall of flod: [0.946236559139785, 0.9368421052631579, 0.925531914893617, 0.9787234042553191, 0.9456521739130435, 0.967391304347826, 0.9347826086956522, 0.9565217391304348, 0.9560439560439561, 0.9130434782608695]
Model train F1 Score of flod: [0.967032967032967, 0.967391304347826, 0.9560439560439561, 0.989247311827957, 0.9666666666666666, 0.9834254143646408, 0.9662921348314606, 0.9617486338797815, 0.9775280898876404, 0.9491525423728814]
Model train Accuracy of flod: [0.9763779527559056, 0.9763779527559056, 0.968503937007874, 0.9921568627450981, 0.9764705882352941, 0.9882352941176471, 0.9764705882352941, 0.9725490196078431, 0.984313725490196, 0.9647058823529412]
模型训练集Precision:0.9921305033367162,Recall:0.9460769243943661,F1_Score:0.9684529021255777,Accuracy:0.9776161803304
Model valid Precision of flod: [1.0, 0.75, 0.7272727272727273, 0.9, 1.0, 1.0, 1.0, 0.8571428571428571, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.75, 0.8888888888888888, 1.0, 0.5454545454545454, 0.5454545454545454, 0.8181818181818182, 0.5454545454545454, 0.6666666666666666, 0.6363636363636364]
Model valid train F1 Score of flod: [0.8235294117647058, 0.75, 0.7999999999999999, 0.9473684210526316, 0.7058823529411764, 0.7058823529411764, 0.9, 0.6666666666666665, 0.8, 0.7777777777777778]
Model valid train Accuracy of flod: [0.896551724137931, 0.8620689655172413, 0.8620689655172413, 0.9642857142857143, 0.8214285714285714, 0.8214285714285714, 0.9285714285714286, 0.7857142857142857, 0.8571428571428571, 0.8571428571428571]
模型Valid集Precision:0.9234415584415585,Recall:0.7096464646464647,F1_Score:0.7877106983144134,Accuracy:0.8656403940886699
模型测试集Precision:0.6415094339622641,Recall:0.7727272727272727,F1_score:0.7010309278350515,Accuracy:0.9849272349272349
-------------------测试集混淆举证-------------------
In [50]:
#输出PR曲线和ROC曲线结果
RondomForest_PR.to_excel('Data/PR Curve/ML/RondomForest_PR.xlsx','UTF-8')
RondomForest_ROC.to_excel('Data/ROC Curve/ML/RondomForest_ROC.xlsx','UTF-8')
In [51]:
#测试XGBoost模型
from xgboost import XGBClassifier
XGBoost=XGBClassifier(n_jobs=-1,verbosity=1,tree_method='auto',gpu_id=0,random_state=2024)
XGBoost_PR,XGBoost_Accuracy,XGBoost_ROC,XGBoost_AUC,XGBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=XGBoost,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.8571428571428571, 0.7, 0.75, 0.8, 0.9, 1.0, 1.0, 0.6923076923076923, 0.8888888888888888, 0.8]
Model valid train Recall of flod: [0.6, 0.875, 1.0, 0.8888888888888888, 0.8181818181818182, 0.7272727272727273, 0.7272727272727273, 0.8181818181818182, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.7777777777777777, 0.8571428571428571, 0.8421052631578948, 0.8571428571428572, 0.8421052631578948, 0.8421052631578948, 0.7500000000000001, 0.761904761904762, 0.761904761904762]
Model valid train Accuracy of flod: [0.8275862068965517, 0.8620689655172413, 0.896551724137931, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.7857142857142857, 0.8214285714285714, 0.8214285714285714]
模型Valid集Precision:0.8388339438339438,Recall:0.7848737373737374,F1_Score:0.7998071158287876,Accuracy:0.8586206896551725
模型测试集Precision:0.42528735632183906,Recall:0.8409090909090909,F1_score:0.564885496183206,Accuracy:0.9703742203742204
-------------------测试集混淆举证-------------------
In [52]:
#输出PR曲线和ROC曲线结果
XGBoost_PR.to_excel('Data/PR Curve/ML/XGBoost_PR.xlsx','UTF-8')
XGBoost_ROC.to_excel('Data/ROC Curve/ML/XGBoost_ROC.xlsx','UTF-8')
In [53]:
#测试LightGBM算法
from lightgbm import LGBMClassifier
LightGBM=LGBMClassifier(boosting_type='gbdt',num_leaves=31,learning_rate=0.01,n_estimators=200,
                        n_jobs=-1,objective='binary',metric='binary_logloss',keep_training_booster=True,
                       importance_type='gini',random_state=2024)
LightGBM_PR,LightGBM_Accuracy,LightGBM_ROC,LightGBM_AUC,LightGBM_ML=RunTrainModelCV(Resmaple=CNNML,Model=LightGBM,Data=MLRunData,Label=ReLabel,cv=10)

resample shape: Counter({0: 180, 1: 103})
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 93, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.366142 -> initscore=-0.548805
[LightGBM] [Info] Start training from score -0.548805
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 95, number of negative: 159
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4236
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.374016 -> initscore=-0.515027
[LightGBM] [Info] Start training from score -0.515027
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 160
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.370079 -> initscore=-0.531879
[LightGBM] [Info] Start training from score -0.531879
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4268
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.368627 -> initscore=-0.538110
[LightGBM] [Info] Start training from score -0.538110
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4303
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4298
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4245
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4269
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 91, number of negative: 164
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4277
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.356863 -> initscore=-0.589007
[LightGBM] [Info] Start training from score -0.589007
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4292
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
Model train Precision of flod: [1.0, 1.0, 1.0, 0.9893617021276596, 0.989010989010989, 1.0, 0.9888888888888889, 1.0, 1.0, 1.0]
Model train Recall of flod: [0.978494623655914, 0.9894736842105263, 1.0, 0.9893617021276596, 0.9782608695652174, 1.0, 0.967391304347826, 0.9891304347826086, 0.978021978021978, 0.9891304347826086]
Model train F1 Score of flod: [0.9891304347826088, 0.9947089947089947, 1.0, 0.9893617021276596, 0.9836065573770493, 1.0, 0.978021978021978, 0.994535519125683, 0.9888888888888888, 0.994535519125683]
Model train Accuracy of flod: [0.9921259842519685, 0.9960629921259843, 1.0, 0.9921568627450981, 0.9882352941176471, 1.0, 0.984313725490196, 0.996078431372549, 0.9921568627450981, 0.996078431372549]
模型训练集Precision:0.9967261580027538,Recall:0.9859265031494339,F1_Score:0.9912789594158546,Accuracy:0.9937208584221089
Model valid Precision of flod: [1.0, 0.7777777777777778, 0.8, 0.6, 0.875, 1.0, 1.0, 0.7, 0.8888888888888888, 0.8333333333333334]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.6666666666666666, 0.6363636363636364, 0.7272727272727273, 0.7272727272727273, 0.6363636363636364, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.8235294117647058, 0.823529411764706, 0.8421052631578948, 0.631578947368421, 0.7368421052631579, 0.8421052631578948, 0.8421052631578948, 0.6666666666666666, 0.761904761904762, 0.8695652173913043]
Model valid train Accuracy of flod: [0.896551724137931, 0.896551724137931, 0.896551724137931, 0.75, 0.8214285714285714, 0.8928571428571429, 0.8928571428571429, 0.75, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.8475000000000001,Recall:0.7433585858585859,F1_Score:0.7839932311597407,Accuracy:0.8511083743842365
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 103, number of negative: 180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4619
[LightGBM] [Info] Number of data points in the train set: 283, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.363958 -> initscore=-0.558228
[LightGBM] [Info] Start training from score -0.558228
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
模型测试集Precision:0.47297297297297297,Recall:0.7954545454545454,F1_score:0.5932203389830508,Accuracy:0.975051975051975
-------------------测试集混淆举证-------------------
[LightGBM] [Warning] Unknown parameter: keep_training_booster

[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
In [54]:
#输出PR曲线和ROC曲线结果
LightGBM_PR.to_excel('Data/PR Curve/ML/LightGBM_PR.xlsx','UTF-8')
LightGBM_ROC.to_excel('Data/ROC Curve/ML/LightGBM_ROC.xlsx','UTF-8')
In [55]:
#测试CatBoost算法
from catboost import CatBoostClassifier
CatBoost=CatBoostClassifier(learning_rate=0.01,depth=6,iterations=500,thread_count=-1)
CatBoost_PR,CatBoost_Accuracy,CatBoost_ROC,CatBoost_AUC,CatBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=CatBoost,Data=MLRunData,Label=ReLabel,cv=10)

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Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.875, 0.7777777777777778, 0.8888888888888888, 0.8888888888888888, 1.0, 1.0, 1.0, 0.7692307692307693, 1.0, 0.9090909090909091]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.8888888888888888, 0.8181818181818182, 0.8181818181818182, 0.8181818181818182, 0.9090909090909091, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.7777777777777777, 0.823529411764706, 0.8888888888888888, 0.8888888888888888, 0.9, 0.9, 0.9, 0.8333333333333333, 0.8, 0.9090909090909091]
Model valid train Accuracy of flod: [0.8620689655172413, 0.896551724137931, 0.9310344827586207, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.8571428571428571, 0.8571428571428571, 0.9285714285714286]
模型Valid集Precision:0.9108877233877232,Recall:0.8292171717171717,F1_Score:0.8621509209744505,Accuracy:0.9046798029556651
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模型测试集Precision:0.6333333333333333,Recall:0.8636363636363636,F1_score:0.7307692307692307,Accuracy:0.9854469854469855
-------------------测试集混淆举证-------------------
In [56]:
#输出PR曲线和ROC曲线结果
CatBoost_PR.to_excel('Data/PR Curve/ML/CatBoost_PR.xlsx','UTF-8')
CatBoost_ROC.to_excel('Data/ROC Curve/ML/CatBoost_ROC.xlsx','UTF-8')
In [57]:
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(CatBoost_PR['recall'], CatBoost_PR['precision'],color='red',label='CatBoost=%f'%CatBoost_Accuracy)
PR_ax.plot(DTC_PR['recall'], DTC_PR['precision'],color='green',label='DecisionTree=%f'%DTC_Accuracy)
PR_ax.plot(LightGBM_PR['recall'], LightGBM_PR['precision'],color='blue',label='LightGBM=%f'%LightGBM_Accuracy)
PR_ax.plot(Logistic_PR['recall'], Logistic_PR['precision'],color='yellow',label='Logistic=%f'%Logistic_Accuracy)
PR_ax.plot(RondomForest_PR['recall'], RondomForest_PR['precision'],color='purple',label='RondomForest=%f'%RondomForest_Accuracy)
PR_ax.plot(SVM_PR['recall'], SVM_PR['precision'],color='cyan',label='SVM=%f'%SVM_Accuracy)
PR_ax.plot(XGBoost_PR['recall'], XGBoost_PR['precision'],color='pink',label='XGBoost=%f'%XGBoost_Accuracy)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')  
PR_ax.set_ylabel('Precision')    
plt.legend(loc="best")  
plt.show()
In [58]:
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(CatBoost_ROC['fpr'], CatBoost_ROC['tpr'],color='red',label='CatBoost=%f'%CatBoost_AUC)
ROC_ax.plot(DTC_ROC['fpr'], DTC_ROC['tpr'],color='green',label='DecisionTree=%f'%DTC_AUC)
ROC_ax.plot(LightGBM_ROC['fpr'], LightGBM_ROC['tpr'],color='blue',label='LightGBM=%f'%LightGBM_AUC)
ROC_ax.plot(Logistic_ROC['fpr'], Logistic_ROC['tpr'],color='yellow',label='Logistic=%f'%Logistic_AUC)
ROC_ax.plot(RondomForest_ROC['fpr'], RondomForest_ROC['tpr'],color='purple',label='RondomForest=%f'%RondomForest_AUC)
ROC_ax.plot(SVM_ROC['fpr'], SVM_ROC['tpr'],color='cyan',label='SVM=%f'%SVM_AUC)
ROC_ax.plot(XGBoost_ROC['fpr'], XGBoost_ROC['tpr'],color='pink',label='XGBoost=%f'%XGBoost_AUC)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])  
ROC_ax.set_ylim([0, 1.05])  
ROC_ax.set_xlabel('FPR')  
ROC_ax.set_ylabel('TPR')    
plt.legend(loc="best")  
plt.show()
In [59]:
#保存模型-输入[batchs,70],输出[batch,1]
MLModel=[Logistic_ML,SVM_ML,DTC_ML,Forest_ML,XGBoost_ML,LightGBM_ML,CatBoost_ML]
MLModelStr=['Logistic.pkl','SVM.pkl','DecisionTree.pkl','Forest.pkl','XGBoost.pkl','LightGBM.pkl','CatBoost.pkl']
try:
    for model,modelstr in zip(MLModel,MLModelStr):
        joblib.dump(model,'ML Models/Trainer/'+modelstr)
    print('模型保存成功！')
except:
    print('模型保存异常！！！')

模型保存成功！

载入SHAP库计算特征重要性

In [126]:
#使用PCA算法计算95%可解释方差比维度数
from sklearn.decomposition import PCA
PCA=PCA(n_components=0.95)
Methylation_99=PCA.fit_transform(MLRunData)    #数据降维
Methylation_99.shape
Out[126]:
(6413, 33)

使用SHAP库计算获得CatBoost模型特征重要性

In [127]:
#复现CatBoost模型训练额测试数据集
CatBoost_train,CatBoost_test,CatBoost_trainlabel,CatBoost_testlabel=train_test_split(MLRunData,ReLabel,stratify=ReLabel,train_size=0.7,random_state=2024)
In [135]:
import shap
CatExplainer=shap.TreeExplainer(CatBoost_ML)
CatBoost_shapvalues=CatExplainer.shap_values(CatBoost_test)
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33)
In [136]:
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33,plot_type='bar')
In [138]:
#绘制heatmap图
SHAP_CatBoost=CatExplainer(CatBoost_test)
plt.figure(dpi=300)
shap.plots.heatmap(SHAP_CatBoost,max_display=34)
plt.show()
In [139]:
#绘制Decision图
expected_value=CatExplainer.expected_value
shap.decision_plot(expected_value,CatBoost_shapvalues,CatBoost_test.columns,feature_display_range=slice(None, -34, -1))
In [144]:
shap.plots.scatter(SHAP_CatBoost[:,'age'])
In [165]:
shap.plots.scatter(SHAP_CatBoost[:,'age'],color=SHAP_CatBoost)
shap.plots.scatter(SHAP_CatBoost[:,'gender_encode'],color=SHAP_CatBoost)
In [143]:
#输出SHAP值
SHAPData=DataFrame(CatBoost_shapvalues)
SHAPData.to_excel('Data/SHAP Values/CatBoost_SHAP_Data.xlsx','UFT-8')
SHAPData.to_csv('Data/SHAP Values/CatBoost_SHAP_Data.csv')

使用LIME（Local Interpretable Model-agnostic Explanations）对SVM模型进行模型解释性。

In [145]:
#转化为Numpy数组
import numpy as np
TestDataLIME=np.array(CatBoost_test)
TestDataLIME
Out[145]:
array([[0.46083596, 0.72218471, 0.67574463, ..., 0.36230225, 0.66666667,
        0.        ],
       [0.38295897, 0.50947919, 0.61430678, ..., 0.46848894, 0.27192982,
        1.        ],
       [0.33958682, 0.69191957, 0.65616158, ..., 0.40703271, 0.72807018,
        1.        ],
       ...,
       [0.33958682, 0.59925265, 0.61928701, ..., 0.34645684, 0.47368421,
        1.        ],
       [0.42417219, 0.72249519, 0.67450701, ..., 0.44286982, 0.18421053,
        0.        ],
       [0.39630923, 0.69161445, 0.66946837, ..., 0.41543005, 0.27116053,
        0.        ]])
In [148]:
#构建LIME解释器
from lime.lime_tabular import LimeTabularExplainer
CatBoost_LIMEExplainer=LimeTabularExplainer(training_data=TestDataLIME,mode='classification',feature_names=CatBoost_test.columns,class_names=[0,1], discretize_continuous=True)
In [149]:
CatBoost_test.shape
Out[149]:
(1924, 72)
In [150]:
#提取卒中患者和健康人数据——为数据生成解释
LIMEData=CatBoost_test
LIMEData['disease_encode']=np.array(CatBoost_testlabel)
#LIMEData=pd.concat([CatBoost_test,CatBoost_testlabel],axis=1,ignore_index=False)    #合并数据
Contral_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==0,:]    #正常组数据
Contral_DataSample=Contral_Data.iloc[0,:-1]
Stroke_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==1,:]    #正常组数据
Stroke_DataSample=Stroke_Data.iloc[1,:-1]
In [163]:
#为健康人生成可解释性
Contral_Explain=CatBoost_LIMEExplainer.explain_instance(Contral_DataSample,CatBoost.predict_proba,num_features=33)
#可视化解释结果
Contral_Explain.show_in_notebook(show_table=True, show_all=False)
Contral_Explain.as_pyplot_figure()
Out[163]:
In [160]:
Control_List=Contral_Explain.as_list()
Control_STR=[]
for feature, weight in Control_List:
    print(f"特征名称: {feature}, 权重: {weight}")
    Control_STR.append(feature)

特征名称: age > 0.63, 权重: 0.14865150574161518
特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425
特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313
特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709
特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463
特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577
特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008
特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517
特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457
特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256
特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686
特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877
特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827
特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702
特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703
特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248
特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338
特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897
特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364
特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131
特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636
特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192
特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126
特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949
特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211
特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464
特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171
特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255
特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191
特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926
特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678
特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464
特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
In [161]:
#为卒中患者生成可解释性
Stroke_Explain=CatBoost_LIMEExplainer.explain_instance(Stroke_DataSample,CatBoost.predict_proba,num_features=29)
#可视化解释结果
Stroke_Explain.show_in_notebook(show_table=True, show_all=False)
Stroke_Explain.as_pyplot_figure()
Out[161]:
In [162]:
Stroke_List=Stroke_Explain.as_list()
Stroke_STR=[]
for feature, weight in Control_List:
    print(f"特征名称: {feature}, 权重: {weight}")
    Stroke_STR.append(feature)

特征名称: age > 0.63, 权重: 0.14865150574161518
特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425
特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313
特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709
特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463
特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577
特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008
特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517
特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457
特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256
特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686
特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877
特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827
特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702
特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703
特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248
特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338
特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897
特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364
特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131
特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636
特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192
特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126
特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949
特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211
特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464
特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171
特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255
特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191
特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926
特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678
特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464
特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
In [164]:
Stroke_STR
Out[164]:
['age > 0.63',
 '0.50 < cg05876899 <= 0.52',
 'cg21459545 > 0.75',
 'cg08291693 <= 0.49',
 '0.79 < cg22328457 <= 0.94',
 'cg01446576 <= 0.53',
 '0.34 < cg00328972 <= 0.36',
 'cg01717973 <= 0.41',
 '0.70 < cg00958409 <= 0.73',
 '0.55 < cg01550055 <= 0.60',
 'cg09134165 > 0.62',
 'cg19214916 > 0.91',
 '0.53 < cg20493887 <= 0.57',
 '0.65 < cg21939789 <= 0.71',
 '0.44 < cg00716675 <= 0.46',
 'cg01101221 <= 0.59',
 '0.62 < cg03977822 <= 0.66',
 'cg12914043 > 0.56',
 'gender_encode <= 0.00',
 'cg09834142 > 0.67',
 '0.85 < cg09229960 <= 0.89',
 'cg15534364 > 0.42',
 '0.66 < cg00817100 <= 0.68',
 '0.55 < cg17488844 <= 0.64',
 '0.31 < cg01522249 <= 0.39',
 'cg27260927 > 0.72',
 '0.55 < cg17917970 <= 0.71',
 'cg27353825 > 0.44',
 'cg19267533 > 0.59',
 'cg00962755 > 0.44',
 'cg25967516 > 0.56',
 '0.61 < cg09060772 <= 0.64',
 '0.41 < cg27636290 <= 0.45']

获取LIME核SHAP数据交集

In [9]:
SHAP_CG=["cg05876899","cg00958409","cg08882363","cg21459545","cg00328972","cg01550055","cg21939789","cg01446576","cg17917970","cg20493887",
         "cg22328457","cg12914043","cg08291693","cg09834142","cg00941229","cg01717973","cg06834235","cg09134165","cg00817100","cg00716675",
         "cg17488844","cg07810091","cg12377701","cg23429746","cg32524254","cg01370179","cg07086565","cg13192819","cg19214916","cg24606370",
         "cg25967516","cg09307104","cg05876899"]
LIME_CG=['cg05876899', 'cg21459545', 'cg08291693', 'cg22328457', 'cg01446576','cg00328972', 'cg01717973', 'cg00958409', 'cg01550055', 'cg09134165',
         'cg19214916', 'cg20493887', 'cg21939789', 'cg00716675', 'cg01101221','cg03977822', 'cg12914043', 'cg09834142', 'cg09229960', 'cg15534364',
         'cg00817100', 'cg17488844', 'cg01522249', 'cg27260927', 'cg17917970','cg27353825', 'cg19267533', 'cg00962755', 'cg25967516', 'cg09060772',
         'cg27636290','cg23429746','cg05876899']
Feature_CG=set(SHAP_CG)&set(LIME_CG)    #计算交集
Feature_CG
Out[9]:
{'cg00328972',
 'cg00716675',
 'cg00817100',
 'cg00958409',
 'cg01446576',
 'cg01550055',
 'cg01717973',
 'cg05876899',
 'cg08291693',
 'cg09134165',
 'cg09834142',
 'cg12914043',
 'cg17488844',
 'cg17917970',
 'cg19214916',
 'cg20493887',
 'cg21459545',
 'cg21939789',
 'cg22328457',
 'cg23429746',
 'cg25967516'}
In [173]:
from matplotlib import pyplot as plt
from matplotlib_venn import venn2
plt.figure(figsize=(8, 8))
venn2([set(SHAP_CG),set(LIME_CG)], set_labels=('Features in SHAP', 'Features in LIME'))
plt.show()

对数据进行统计学分析

In [10]:
StatData=MethylationData.loc[:,list(Feature_CG)+['gender','age','disease']]
StatData.head(10)
Out[10]:
cg00716675 cg00958409 cg01550055 cg17488844 cg08291693 cg25967516 cg21459545 cg09834142 cg19214916 cg05876899 ... cg00817100 cg01717973 cg21939789 cg12914043 cg09134165 cg22328457 cg01446576 gender age disease
0 -3.837361 -2.616463 -1.585160 -1.909169 2.324893 -0.052001 0.384596 -0.601375 0.200630 2.921730 ... -2.010774 -3.052934 -0.164336 -0.866223 -1.288795 -1.564056 -2.570129 F 78.0 stroke
1 -4.247583 -2.767818 -1.247949 -3.938986 3.100385 -1.657722 -0.645381 -3.938986 -1.557086 3.228904 ... -1.543240 -3.886935 -3.314031 -3.790034 -3.256540 0.000000 -3.744756 M 52.0 stroke
2 -3.993781 -2.375206 -1.536363 -4.489850 2.749976 -1.734052 -0.519260 -4.178048 -1.219330 3.076403 ... -1.710727 -5.273603 -2.698069 -3.201956 -3.744756 -3.580953 -4.051632 M 92.0 stroke
3 -3.790034 -2.555085 -1.757764 -1.695385 3.076403 -1.125184 0.281794 -0.795272 -0.376304 3.374769 ... -2.288480 -4.178048 -0.540693 -2.120936 0.048000 -2.441207 -4.585271 F 87.0 stroke
4 -4.402578 -2.388119 -1.726235 -3.580953 3.007447 -2.079779 -0.397057 -3.580953 -1.848299 3.228904 ... -2.337277 -3.993781 -2.785946 -3.256540 -3.149987 -3.744756 -4.247583 M 87.0 stroke
5 -3.790034 -2.230092 -1.253727 -4.322159 2.881228 -1.856830 0.003999 -2.985388 -1.185564 3.314031 ... -2.020410 -4.112908 -2.648415 -2.804368 -3.100385 -3.701353 -4.112908 M 41.0 stroke
6 -3.790034 -2.715111 -1.271175 -0.772045 2.767818 -0.954183 0.519260 0.019997 -0.924494 3.124904 ... -1.900305 -3.837361 -0.401218 -1.093021 0.156286 -1.114405 -4.585271 F 78.0 stroke
7 -2.749976 -2.749976 -1.324576 -3.790034 3.701353 -2.441207 -0.654254 -3.580953 -2.496772 3.284902 ... -1.330612 -4.051632 -2.482634 -3.837361 -3.076403 -3.314031 -4.322159 M 80.0 stroke
8 -2.427712 -2.648415 -1.157883 -4.690541 3.314031 -2.804368 -0.426255 -4.247583 -1.613763 3.201956 ... -1.146922 -4.322159 -2.401172 -3.406479 -3.406479 0.000000 -4.247583 M 69.0 stroke
9 -2.842141 -2.540233 -0.974195 -5.773449 3.256540 -2.511081 0.084033 -4.112908 -1.373471 3.543689 ... -1.348848 -3.886935 -2.570129 -3.100385 0.000000 0.000000 -3.659672 M 75.0 stroke

10 rows × 24 columns

In [180]:
#进行正态性检验-基于D’Agostino and Pearson’s test 
from scipy.stats import normaltest
Normal_Name,Normal_stat,Normal_Pvalue=[],[],[]
for normal_name in list(Feature_CG)+['age']:
    stats,p_value=normaltest(StatData.loc[:,normal_name])    #正态检验
    Normal_Name.append(normal_name)
    Normal_stat.append(stats)
    Normal_Pvalue.append(p_value)
NormalResut=DataFrame()
NormalResut['Features']=Normal_Name
NormalResut['Stats']=Normal_stat
NormalResut['P value']=Normal_Pvalue
NormalResut
Out[180]:
Features Stats P value
0 cg23429746 128338.014578 0.000000e+00
1 cg19214916 2722.890099 0.000000e+00
2 cg01717973 2679.681287 0.000000e+00
3 cg21459545 3691.925554 0.000000e+00
4 cg20493887 621.263340 1.242739e-135
5 cg01550055 881.708181 3.463389e-192
6 cg01446576 3764.295928 0.000000e+00
7 cg05876899 1797.616451 0.000000e+00
8 cg00817100 5265.657589 0.000000e+00
9 cg09134165 1570.401594 0.000000e+00
10 cg09834142 4664.772845 0.000000e+00
11 cg08291693 3407.019911 0.000000e+00
12 cg17917970 943.887704 1.089906e-205
13 cg25967516 335.598808 1.335503e-73
14 cg22328457 701.152844 5.579490e-153
15 cg00958409 7688.564578 0.000000e+00
16 cg12914043 380.433626 2.454074e-83
17 cg00716675 5795.308748 0.000000e+00
18 cg17488844 865.716145 1.028318e-188
19 cg21939789 46963.872736 0.000000e+00
20 cg00328972 3194.712049 0.000000e+00
21 age 1563.362527 0.000000e+00
In [181]:
NormalResut.to_excel('Data/Analysis Data/NormalTest.xlsx')    #输出正态检验结果
In [185]:
#绘制分布图
import seaborn as sns
for name in list(Feature_CG)+['age']:
    kde=plt.figure()
    sns.distplot(StatData.loc[:,name])
    plt.show()


`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751



`distplot` is a deprecated function and will be removed in seaborn v0.14.0.

Please adapt your code to use either `displot` (a figure-level function with
similar flexibility) or `histplot` (an axes-level function for histograms).

For a guide to updating your code to use the new functions, please see
https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
In [189]:
#使用Mann-Whitney U对卒中患者、对照组进行差异分析
from scipy.stats import mannwhitneyu,kruskal
Diff_name,Mann_W_stats,Mann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Control=StatData.loc[StatData.loc[:,'disease']=='control',:]
for name in list(Feature_CG)+['age']:
    mann_stats,mann_pvalue=mannwhitneyu(Stroke.loc[:,name],Control.loc[:,name],alternative='two-sided')
    Diff_name.append(name)
    Mann_W_stats.append(mann_stats)
    Mann_W_pvalue.append(mann_pvalue)
DiffResult=DataFrame()
DiffResult['Features']=Diff_name
DiffResult['Mann-Whitney U Stats']=Mann_W_stats
DiffResult['Mann-Whitney U Pvalue']=Mann_W_pvalue
DiffResult=DiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
DiffResult
Out[189]:
Features Mann-Whitney U Stats Mann-Whitney U Pvalue
7 cg05876899 757768.0 6.339030e-41
21 age 746114.5 6.539961e-38
15 cg00958409 212382.0 4.768737e-29
3 cg21459545 223154.5 1.027115e-26
5 cg01550055 683678.0 8.623696e-24
14 cg22328457 267557.0 4.269751e-19
6 cg01446576 283942.5 1.678612e-15
12 cg17917970 300048.5 2.240491e-13
9 cg09134165 309546.0 4.232856e-12
20 cg00328972 315722.5 6.231854e-11
2 cg01717973 591836.5 3.151485e-09
11 cg08291693 341291.0 7.604157e-08
0 cg23429746 367458.5 2.721422e-05
1 cg19214916 377703.0 1.885910e-04
8 cg00817100 536568.5 6.124156e-04
4 cg20493887 395897.5 3.540958e-03
17 cg00716675 415576.0 4.261281e-02
13 cg25967516 497331.5 9.736992e-02
16 cg12914043 496806.5 1.022572e-01
18 cg17488844 488567.0 2.066917e-01
10 cg09834142 486461.0 2.428824e-01
19 cg21939789 476969.0 4.592971e-01
In [190]:
DiffResult.to_excel('Data/Analysis Data/StrokeVSControl.xlsx')
In [216]:
#数据标准化和缩放
CorrData=StatData.loc[:,['age','cg05876899']]
Corr_name=CorrData.columns
CorrStand=StandardScaler()    #数据标准化
CorrStand=CorrStand.fit_transform(CorrData)
MinMaxCorr=MinMaxScaler(feature_range=(0,1))    #数据缩放
MinMax=MinMaxCorr.fit_transform(CorrStand)
CorrDataST=DataFrame(MinMax)
CorrDataST.columns=Corr_name
CorrDataST
Out[216]:
age cg05876899
0 0.684211 0.495571
1 0.456140 0.520210
2 0.807018 0.507977
3 0.763158 0.531910
4 0.763158 0.520210
... ... ...
6408 0.000000 0.430502
6409 0.652632 0.511868
6410 0.605263 0.502446
6411 0.298246 0.534453
6412 0.131579 0.515939

6413 rows × 2 columns

In [218]:
#绘制age与cg05876899散点图
from scipy.stats import spearmanr
corr,p_value=spearmanr(CorrDataST.loc[:,'age'],CorrDataST.loc[:,'cg05876899'])
print('Spearman相关系数为:{0},P值为:{1}'.format(corr,p_value))
plt.figure(dpi=300)
sns.jointplot(data=CorrDataST,x='age',y='cg05876899')
plt.show()

Spearman相关系数为:-0.09646944046883249,P值为:9.790722731741588e-15

<Figure size 1800x1200 with 0 Axes>
In [209]:
plt.figure(dpi=300)
sns.barplot(StatData,x='disease',y='cg05876899',errorbar="ci")
plt.show()
In [191]:
#绘制箱型图
for name in list(DiffResult['Features']):
    Box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=name,showmeans=True)
    plt.show()
In [195]:
#使用Mann-Whitney U对男性患者和女性患者进行差异分析
from scipy.stats import mannwhitneyu,kruskal
SexDiff_name,SexMann_W_stats,SexMann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Man=Stroke.loc[Stroke.loc[:,'gender']=='M',:]
Feman=Stroke.loc[Stroke.loc[:,'gender']=='F',:]
for name in list(Feature_CG)+['age']:
    mann_stats,mann_pvalue=mannwhitneyu(Man.loc[:,name],Feman.loc[:,name],alternative='two-sided')
    SexDiff_name.append(name)
    SexMann_W_stats.append(mann_stats)
    SexMann_W_pvalue.append(mann_pvalue)
SexDiffResult=DataFrame()
SexDiffResult['Features']=SexDiff_name
SexDiffResult['Mann-Whitney U Stats']=SexMann_W_stats
SexDiffResult['Mann-Whitney U Pvalue']=SexMann_W_pvalue
SexDiffResult=SexDiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
SexDiffResult
Out[195]:
Features Mann-Whitney U Stats Mann-Whitney U Pvalue
0 cg23429746 55.0 1.184262e-24
10 cg09834142 57.0 1.280125e-24
18 cg17488844 57.5 1.295483e-24
19 cg21939789 71.0 2.245348e-24
3 cg21459545 72.5 2.394466e-24
16 cg12914043 91.0 4.948103e-24
13 cg25967516 129.5 2.259475e-23
1 cg19214916 156.5 6.443147e-23
9 cg09134165 202.0 3.474110e-22
12 cg17917970 310.0 1.973555e-20
14 cg22328457 1397.5 4.413451e-07
21 age 1713.5 1.300201e-04
11 cg08291693 3530.5 1.314165e-03
7 cg05876899 3309.5 1.841224e-02
4 cg20493887 2157.5 3.532568e-02
15 cg00958409 3086.5 1.356811e-01
2 cg01717973 2411.5 2.623936e-01
20 cg00328972 2863.0 5.301055e-01
5 cg01550055 2559.5 5.848453e-01
8 cg00817100 2790.0 7.316775e-01
17 cg00716675 2656.0 8.630851e-01
6 cg01446576 2742.5 8.735049e-01
In [196]:
SexDiffResult.to_excel('Data/Analysis Data/SexDiffResult.xlsx')
In [210]:
plt.figure(dpi=300)
sns.barplot(Stroke,x='gender',y='cg23429746',errorbar="ci")
plt.show()
In [197]:
#绘制箱型图
for name in SexDiffResult['Features']:
    Box=plt.figure(dpi=300)
    sns.boxplot(Stroke,x='gender',y=name,showmeans=True)
    plt.show()
In [12]:
import matplotlib.pyplot as plt
import seaborn as sns
In [14]:
for cg_name in list(Feature_CG)+['age']:
    box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
    plt.show()
In [16]:
#提取卒中患者VS对照组与男性患者与女性患者甲基化数据取交集_[Top 10]
CG_StrokeVSControl=['cg05876899', 'cg00958409', 'cg21459545', 'cg01550055', 'cg22328457', 'cg01446576', 'cg17917970', 
                    'cg09134165', 'cg00328972','cg01717973']
CG_StrokeManVSFeman=['cg23429746', 'cg09834142', 'cg17488844', 'cg21939789', 'cg21459545', 'cg12914043', 'cg25967516', 
                     'cg19214916', 'cg09134165', 'cg17917970']
Get_DiffCG=set(CG_StrokeVSControl)&set(CG_StrokeManVSFeman)
print(Get_DiffCG)

{'cg09134165', 'cg21459545', 'cg17917970'}
In [199]:
#绘制韦恩图
plt.figure(figsize=(6,8))
venn2([set(CG_StrokeVSControl),set(CG_StrokeManVSFeman)],set_labels=('Stroke group vs control group','Male stroke group vs female stroke group'))
plt.show()
In [17]:
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
    box=plt.figure(dpi=300)
    sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
    plt.show()
In [207]:
#绘制条形图
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
    box=plt.figure(dpi=300)
    sns.barplot(StatData,x='disease',y=cg_name,hue='gender',errorbar="ci") #95%CI
    plt.show()
In [ ]: